Classification and Regression Trees (CART) Flashcards

Questions and Answers

What are regression trees used for?

Predicting models (correct)

Data cleaning

Data visualization

Decision making

What are tree models?

Optimization algorithms

Machine learning algorithms (correct)

Data preprocessing techniques

Statistical models

List two reasons why regression trees are used.

They are simple to interpret, and they require little to no data preparation.

What is the root in a tree model?

The starting point of the tree. Signup and view all the answers

Explain binary recursive partitioning.

It splits the dataset in two and keeps the solution that minimizes the within-group variability. Signup and view all the answers

What is cross-validation used for in machine learning?

Evaluating model performance Signup and view all the answers

What does simplification in regression trees involve?

Deciding how much of a model to retain by balancing cross-validation values and complexity parameters. Signup and view all the answers

What is the 1-SE rule?

It tells us at which point adding a new node does not improve the model. Signup and view all the answers

What method is used in random forests?

Regression trees and bagging Signup and view all the answers

What is one advantage of random forests?

Can handle large datasets Signup and view all the answers

Name one limitation of random forests.

They are data and computationally intensive. Signup and view all the answers

What do you need to specify for a random forest?

How many trees to generate, how many predictors per tree, sample size, node size, maximum number of nodes. Signup and view all the answers

What is the purpose of boosted regression trees?

To combine the strengths of regression trees and boosting. Signup and view all the answers

What is one limitation of boosted regression trees?

They need at least two predictor variables to run. Signup and view all the answers

Study Notes

Regression Trees

Used for predictive modeling to establish relationships between variables.
Decision trees that map inputs to outputs, forming a hierarchical structure.

Tree Models

Machine learning algorithms that represent data in a tree-like structure for analysis.
Well-suited for understanding data complexity and interactions.

Benefits of CART

Intuitive and easy to interpret for stakeholders.
Effective for initial exploratory data analysis.
Visual representation aids in understanding variable interactions.
Minimal data preparation needed, accommodating raw datasets.
Handles non-linear relationships well.

Components of Trees

Root: Origin point of the tree where decisions begin.
Splits: Decision points that segment data based on predictor variables.
Leaves: Terminal nodes that provide output predictions.

Multivariate Regression Trees (MRT)

Non-linear, non-parametric models that illustrate relationships between response variables and predictors.
Effective for datasets with complex feature interactions.

MRT Procedures

Involves constrained data partitioning to create decision boundaries.
Employs cross-validation to verify model accuracy.

Binary Recursive Partitioning

Splits datasets into two groups to minimize within-group variability.
Continues until each object is grouped or a minimum threshold is met.

Splits and Purity

Splits are evaluated by their "purity," determining how well they create homogeneous subsets from the data.

Cross-Validation

Evaluates model performance on unseen data by partitioning datasets into training and testing sets.
Commonly uses a 70/30 ratio for training/testing data allocation.

Model Simplification

Involves deciding the number of splits to retain based on model performance versus complexity.
Balances cross-validation scores against the cost of additional variables.

1-SE Rule

Helps determine when adding additional nodes does not enhance model performance.

Random Forest

Ensemble method combining multiple regression trees with bagging techniques for improved prediction accuracy.

Random Forest Advantages

Versatile with different response types (binomial, Gaussian, Poisson).
Stochastic nature enhances predictive performance.
Higher accuracy achieved through robust cross-validation.
Resilient to missing values and suitable for high-dimensional datasets.

Random Forest Limitations

Sensitive to class prevalence in datasets, affecting generalization.
Requires substantial computational resources and large datasets.
Struggles with sparse data or datasets lacking clear decision boundaries.

Random Forest Specifications

Number of trees to generate.
Number of predictors to consider per tree.
Sample size and node size specifications to refine model structure.

Boosted Regression Trees (BRT)

Combines regression trees with boosting to enhance prediction accuracy sequentially.
Each tree corrects errors from the previous ones.

BRT Advantages

Works well with a variety of response types (binomial, Gaussian, Poisson).
Automatically identifies optimal model fit without requiring extensive preprocessing.
Effectively accounts for predictor interactions and is robust against outliers.

BRT Limitations

Requires at least two predictor variables to function.
Data-intensive, necessitating numerous observations and trees.
Sensitive to model specifications and may produce less interpretable outputs.
Can disproportionately impact model performance due to outliers.

Description

This quiz features flashcards focused on Classification and Regression Trees (CART). You'll learn key terms such as regression trees and tree models, along with their applications in data analysis. Great for anyone looking to solidify their understanding of these important machine learning concepts.